Closed-Loop Medication System: Leveraging Technology to Elevate Safety
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Healthcare organizations have long been dependent on the vigilance of nurses to identify and intercept medication errors before they can adversely affect patients. New technologies have been implemented in an effort to reduce medication errors; however, few studies have evaluated the long-term effects of technology-based interventions in reducing medication errors. AIM: The aim of this study was to evaluate the effects of barcode medication administration (BCMA) and the closed-loop medication system (CLMS) interventions on medication errors and adverse drug event (ADE) rates. METHODS: An autoregressive integrated moving average model for interrupted time series design was used to evaluate the impact of the BCMA and CLMS interventions on the monthly reported medication error and ADE rates at Humber River Hospital between September 2013 and August 2018. Descriptive statistics were generated to evaluate the types of error and their gravity. RESULTS: A total of 1,712 medication errors and ADEs were reported in the five-year study period. The results of the interrupted time series indicated that the introduction of the BCMA intervention was associated with a statistically significant gradual decrease in reported medication error and ADE rates at 0.002 percentage points per month (p = 0.003). The introduction of the CLMS intervention was associated with an immediate absolute decrease in reported medication error and ADE rates of 0.010% (p = 0.020). CONCLUSIONS: The findings from this study support the adoption of both BCMA and CLMS interventions to prevent medication errors. Staged implementation of CLMS allows time for learning and incorporating barcode scanning. Interprofessional and cross-functional collaboration is necessary to successfully integrate the requirements of each respective discipline and service in the CLMS.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it